Transformers Don't In-Context Learn Least Squares Regression

Joshua Hill, Benjamin Eyre, Elliot Creager

Keywords: In-Context Learning, Out‑of‑Distribution Generalization, Robustness

TL;DR: Transformers trained for in-context linear regression do not learn generalizable algorithms like OLS and fail under distribution shifts, which manifest as distinct “spectral signatures” in their residual-stream geometry.

Abstract: In-context learning (ICL) has emerged as a powerful capability of large pretrained transformers, enabling them to solve new tasks implicit in example input–output pairs without any gradient updates. Despite its practical success, the mechanisms underlying ICL remain largely mysterious. In this work we study synthetic linear regression to probe how transformers implement learning at inference time. Previous works have demonstrated that transformers match the performance of learning rules such as Ordinary Least Squares (OLS) regression or gradient descent and have suggested ICL is facilitated in transformers through the learned implementation of one of these techniques. In this work, we demonstrate through a suite of out‑of‑distribution generalization experiments that transformers trained for ICL fail to generalize after shifts in the prompt distribution, a behaviour that is inconsistent with the notion of transformers implementing algorithms such as OLS. Finally, we highlight the role of the pretraining corpus in shaping ICL behaviour through a spectral analysis of the learned representations in the residual stream. Inputs from the same distribution as the training data produce representations with a unique \textit{spectral signature}: inputs from this distribution tend to have the same top two singular vectors. This spectral signature is not shared by out-of-distribution inputs, and a metric characterizing the presence of this signature is highly correlated with low loss.

Submission Number: 141